
"""simple docstring"""






import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save




def __magic_name__	(				_lowerCamelCase:		Dict,					_lowerCamelCase:		Dict,					_lowerCamelCase:		Union[str, Any]=1_024,					_lowerCamelCase:		Tuple=1_024,					_lowerCamelCase:		Optional[int]=False,					**_lowerCamelCase:		Optional[int]			)					->						Any:

	'''simple docstring'''




	lowerCAmelCase												=  AutoTokenizer.from_pretrained(_lowerCamelCase			)
	lowerCAmelCase												=  SeqaSeqDataset(_lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase,					type_path='''train''',					**_lowerCamelCase			)
	lowerCAmelCase												=  tok.pad_token_id

	def get_lens(_lowerCamelCase:		List[Any]			):
		lowerCAmelCase												=  tqdm(
		    DataLoader(_lowerCamelCase,					batch_size=512,					num_workers=8,					shuffle=_lowerCamelCase,					collate_fn=ds.collate_fn			),					desc=str(ds.len_file			),					)
		lowerCAmelCase												=  []
		for batch in dl:
			lowerCAmelCase												=  batch['''input_ids'''].ne(_lowerCamelCase			).sum(1			).tolist()
			lowerCAmelCase												=  batch['''labels'''].ne(_lowerCamelCase			).sum(1			).tolist()
			if consider_target:
				for src, tgt in zip(_lowerCamelCase,					_lowerCamelCase			):
					max_lens.append(max(_lowerCamelCase,					_lowerCamelCase			)			)
			else:
				max_lens.extend(_lowerCamelCase			)
		return max_lens

	lowerCAmelCase												=  get_lens(_lowerCamelCase			)
	lowerCAmelCase												=  SeqaSeqDataset(_lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase,					type_path='''val''',					**_lowerCamelCase			)
	lowerCAmelCase												=  get_lens(_lowerCamelCase			)
	pickle_save(_lowerCamelCase,					train_ds.len_file			)
	pickle_save(_lowerCamelCase,					val_ds.len_file			)


if __name__ == "__main__":
					fire.Fire(save_len_file)




"""simple docstring"""






import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class 			lowercase					( lowercase__		):
			lowercase        =     ['''image_processor''', '''tokenizer''']
			lowercase        =     '''CLIPImageProcessor'''
			lowercase        =     ('''XLMRobertaTokenizer''', '''XLMRobertaTokenizerFast''')






			def __init__(self  :	Union[str, Any]      ,SCREAMING_SNAKE_CASE_  :	int=None      ,SCREAMING_SNAKE_CASE_  :	str=None      ,**SCREAMING_SNAKE_CASE_  :	Union[str, Any]			)       ->	int:





				"""simple docstring"""


				lowerCAmelCase												=  None
				if "feature_extractor" in kwargs:
					warnings.warn(
					    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
					    ''' instead.'''      ,SCREAMING_SNAKE_CASE_      ,)
					lowerCAmelCase												=  kwargs.pop('''feature_extractor'''			)

				lowerCAmelCase												=  image_processor if image_processor is not None else feature_extractor
				if image_processor is None:
					raise ValueError('''You need to specify an `image_processor`.'''			)
				if tokenizer is None:
					raise ValueError('''You need to specify a `tokenizer`.'''			)

				super().__init__(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)






			def __call__(self  :	Union[str, Any]      ,SCREAMING_SNAKE_CASE_  :	List[str]=None      ,SCREAMING_SNAKE_CASE_  :	Optional[int]=None      ,SCREAMING_SNAKE_CASE_  :	Union[str, Any]=None      ,**SCREAMING_SNAKE_CASE_  :	Any			)       ->	List[Any]:





				"""simple docstring"""


				if text is None and images is None:
					raise ValueError('''You have to specify either text or images. Both cannot be none.'''			)

				if text is not None:
					lowerCAmelCase												=  self.tokenizer(SCREAMING_SNAKE_CASE_      ,return_tensors=SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_			)

				if images is not None:
					lowerCAmelCase												=  self.image_processor(SCREAMING_SNAKE_CASE_      ,return_tensors=SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_			)

				if text is not None and images is not None:
					lowerCAmelCase												=  image_features.pixel_values
					return encoding
				elif text is not None:
					return encoding
				else:
					return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE_			)      ,tensor_type=SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	List[str]      ,*SCREAMING_SNAKE_CASE_  :	Union[str, Any]      ,**SCREAMING_SNAKE_CASE_  :	Tuple			)       ->	Optional[Any]:





				"""simple docstring"""


				return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	Union[str, Any]      ,*SCREAMING_SNAKE_CASE_  :	int      ,**SCREAMING_SNAKE_CASE_  :	Union[str, Any]			)       ->	Any:





				"""simple docstring"""


				return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_			)






			@property
			def 					UpperCAmelCase							(self  :	List[Any]			)       ->	Dict:





				"""simple docstring"""


				lowerCAmelCase												=  self.tokenizer.model_input_names
				lowerCAmelCase												=  self.image_processor.model_input_names
				return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)


